Exploit Bounding Box Annotations for Multi-label Object Recognition

Understand

Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications.

• However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal.
• In this paper, we incorporate local information to enhance the feature discriminative power.
• In particular, we first extract object proposals from each image.